class_docstr: Perceptron function
  
  Parameters
  ----------
  x: np.ndarray
      the input data
  y: np.ndarray
      the data to predict from the input data
docstr: Compute perceptron
docstr_tok: ["Compute","perceptron"]
code: def _compute_loss(self, x: tp.ArrayLike) -> float:
      """Compute perceptron"""
      gx = self.apply(x)
      return float(np.mean((gx - self._y) ** 2))
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 4 for calling the Perceptron obj's underlying member method code with example usage: obj._compute_loss(x) and returns: float